Method and system for rating patents and other intangible assets

ABSTRACT

A statistical patent rating method and system is provided for independently assessing the relative breadth (“B”), defensibility (“D”) and commercial relevance (“R”) of individual patent assets and other intangible intellectual property assets. The invention provides new and valuable information that can be used by patent valuation experts, investment advisors, economists and others to help guide future patent investment decisions, licensing programs, patent appraisals, tax valuations, transfer pricing, economic forecasting and planning, and even mediation and/or settlement of patent litigation lawsuits. In one embodiment the invention provides a statistically-based patent rating method and system whereby relative ratings or rankings are generated using a database of patent information by identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population. For example, a first population of patents having a known relatively high intrinsic value or quality (e.g. successfully litigated patents) is compared to a second population of patents having a known relatively low intrinsic value or quality (e.g. unsuccessfully litigated patents). Based on a statistical comparison of the two populations, certain characteristics are identified as being more prevalent or more pronounced in one population group or the other to a statistically significant degree. Multiple such statistical comparisons are used to construct and optimize a computer model or computer algorithm that can then be used to predict and/or provide statistically-accurate probabilities of a desired value or quality being present or a future event occurring, given the identified characteristics of an individual patent or group of patents.

RELATED APPLICATIONS

This application claims priority under 35 USC §120 to, and is acontinuation of, U.S. application Ser. No. 09/661,765, filed Sep. 14,2000 (now U.S. Pat. No. 6,556,992), which claims the benefit under 35USC §119(e) of U.S. Provisional Application No. 60/154,066, filed Sep.14, 1999.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the field of asset valuation and, inparticular, to the field of valuing or rating patents and otherintellectual property assets.

2. Description of the Related Art

Patents play an important role in our economy in encouraging privateinvestment in the development of new technologies that improveproductivity and quality of life for everyone. Each year more than aquarter-million patent applications are filed in the United StatesPatent and Trademark Office (“PTO”) resulting annually in the issuanceof over a hundred fifty-thousand patents. Patent owners and applicantspay combined annual fees and costs of nearly a billion dollars (about$6,700 per issued patent) to the PTO to prosecute and maintain theirpatents and applications. This does not include the additional fees andcosts expended for related professional services, such as attorneys feesand drafting charges.

In addition, each year thousands of patent infringement suits arebrought in the federal courts seeking to enforce patent rights. In the12 months ended Jun. 30, 1992, U.S. federal district courts heard atotal of 1407 such patent cases through trial. See V. Savikas, “SurveyLets Judges Render Some Opinions About the Patent Bar,” Nat'l L. J.,Jan. 18, 1993, at 57. A recent survey conducted by the AmericanIntellectual Property Law Associations (“AIPLA”) reported that themedian cost of patent litigation for each side through trial was about$650,000. AIPLA, “Report of Economic Survey” (1991). Other more recentestimates place the cost of patent enforcement litigation somewhere inthe range of about $1 million per side through trial. Thus, theaggregate annual cost for obtaining, maintaining and enforcing patentsin the United States is easily in the multiple billions of dollars.Similar costs are incurred by patentees in various other foreigncountries where patents may be obtained and enforced.

Because of the great importance of patents in the both the U.S. andglobal economies there has been continued interest in quantifying theintrinsic value of patents and their contribution to economic prosperityof the individuals or companies that hold and/or control them. Suchinformation can be useful for a variety of purposes. For example, patentholders themselves may be interested in using such information to helpguide future decision-making or for purposes of tax treatment, transferpricing or settlement of patent license disputes. Financial advisors andinvestors may seek to use such information for purposes of comparativevalue analysis and/or to construct measures of the “fundamental value”of publicly traded companies for purposes of evaluating possiblestrategic acquisitions or as a guide to investment. Economists may seekto use patent valuations for purposes of economic forecasting andplanning. Insurance carriers may use such valuations to set insurancepolicy premiums and the like for insuring intangible assets. See, e.g.,U.S. Pat. No. 6,018,714, incorporated herein by reference.

However, accurate valuing of patents and other intangible intellectualproperty assets is a highly difficult task and requires an understandingof a broad range of legal, technical and accounting disciplines.Intellectual property assets are rarely traded in open financial marketsor sold at auction. They are intangible assets that secure uniquebenefits to the individuals or companies that hold them and/or exploitthe underlying products or technology embodying the intellectualproperty. In the case of patent assets, for example, this unique valuemay manifest itself in higher profit margins for patented products,increased market power and/or enhanced image or reputation in theindustry and/or among consumers or investors. These and othercharacteristics of intellectual property assets make such assetsextremely difficult to value.

Intellectual property valuation specialists have traditionally employedthree main approaches for valuing patents and other intangibleintellectual property assets. These are: (1) the cost-basis approach;(2) the market approach; and (3) the income approach. See, generally,Smith & Par, Valuation of Intellectual Property and Intangible Assets,2^(nd) Ed. 1989. Each of these traditional accounting-based approachesproduces a different measure or estimate of the intrinsic value of aparticular intellectual property asset in question. The choice of whichapproach is appropriate to use in a given circumstance for a given assetis typically determined by a professional accountant or valuationspecialist, taking into consideration a variety of underlyingassumptions, type of intellectual property asset(s) involved, and howsuch asset(s) are to be employed or exploited. Each of these approachesand the limitations associated therewith are briefly discussed below.

Cost Basis Approach

The first and simplest approach is the so-called cost-basis approach.This approach is often used for tax appraisal purposes or for simple“book value” calculations of a company's net assets. Underlying thisvaluation method is the basic assumption that intellectual propertyassets, on average, have a value roughly equal to their cost-basis. Thesupporting rationale is that individuals and companies invest inintellectual property asset(s) only when the anticipated economicbenefits of the rights to be secured by the intellectual propertyasset(s) exceed the anticipated costs required to obtain the asset(s),taking into account appropriate risk factors, anticipated rates ofreturn, etc. In theory, a rational economic decision-maker would notinvest in a patent or other intellectual property asset if he or she didnot believe that it would produce expected economic benefits (tangibleor otherwise) at least equal to its anticipated cost-basis.

There are several drawbacks or limitations associated with thecost-basis valuation approach which limit its general applicability. Onesignificant drawback is that the approach assumes a rational economicdecision-maker. While such assumption might be statistically valid on amacro scale where many individual decisions and decision-makers areimplicated (e.g., valuing all patents or a large subset of all patents),it is not necessarily a valid assumption when conducting valuationanalysis on a micro scale (e.g., valuing a single patent or a portfolioof patents). It is one thing to assume that, on average, individualinvestment decisions and decision-makers are rational and economicallymotivated. It is a wholly different thing to assume that “each”investment decision or decision-maker is rational and economicallymotivated.

For a variety of reasons certain individuals or companies may investuneconomically in patents or other intellectual property assets—forexample, to achieve personal recognition or to superficially “dress up”balance sheets to attract potential investors or buyers. A variety ofindividual psychological factors may also influence investment decisionsproducing sometimes irrational or non-economical decisions. For example,the so-called “lottery effect” may encourage some individuals orcompanies to over-invest in highly speculative technologies that havethe seductive allure of potentially huge economic rewards, but verylittle if any probability of success. Yet others may investuneconomically in patents and/or other intellectual property assetsbecause of fundamental misunderstandings or misinformation concerningthe role of intellectual property and how it can be realistically andeffectively exploited.

But even assuming a well-informed, rational, economically-motivateddecision-maker, the cost-basis approach is still susceptible to inherentuncertainties in the decision-maker's informed and honest projections ofthe anticipated economic benefits to be gained by a patent or otherintellectual property asset. These benefits are often unknown even tothe patentee until well after the patent has been applied for and oftennot until long after the patent has issued. Many new inventions that maylook promising on paper or in the laboratory turn out to be economicallyor commercially infeasible for a variety of reasons and, as a result,patents covering such inventions may have little if any ultimateintrinsic economic value. Other inventions that may seem only marginalat the time the patent is applied for may turn out to be extremelyvaluable and, if a broad scope of protection is obtained, may returneconomic benefits far in excess of the cost-basis of the patent. Thecost basis approach thus fails to differentiate between these twoextremes because (all other things being equal) the cost basis is thesame for securing a patent on the worthless invention as it is forsecuring a patent on the valuable invention.

The cost-basis approach also does not account for the possibility ofevolution of products and technology over time and changing business andeconomic conditions. Rather, the cost-basis approach implicitly assumesa static business and economic environment, providing a fixed valuebased on actual costs expended at the time of the initial investmentwithout taking into account how the value of that investment mightchange over time. As a result of these and other short-comings, thecost-basis approach has only limited utility as a method for accuratelyestimating the intrinsic economic value of patents or other intellectualproperty assets in real-world business environments.

Market Approach

The second traditional valuation approach—the market approach—seeks toprovide real-world indications of value by studying transactions ofsimilar assets occurring in free and open markets. In theory, the marketapproach can provide very accurate measures or estimates of intrinsicvalue. In practice, however, there are very few open financial marketsthat support active trading of intellectual property and other similarintangible assets. Most intellectual property assets are bought or soldin private transactions involving sales of entire businesses or portionsof businesses. And even if the financial particulars of each suchtransaction were readily available, it would be difficult, if notimpossible, to disaggregate the intellectual property assets involved inthe transaction from the other assets and allocate an appropriate valueto them.

As a result of these and other practical difficulties, there ispresently very little direct real-World data on which to base marketcomparisons of intellectual property and other similar intangibleassets. Nevertheless, several interesting studies have been reportedwhich attempt indirectly to extract market-based valuations of patentsand other intellectual property assets by studying the stock prices ofvarious publicly traded companies that hold such assets. See, Hall,“Innovation and Market Value,” Working Paper No. 6984 NBER (1999); andCockburn et al., “Industry Effects and Appropriability Measures in theStock Market's Valuation of R&D and Patents,” Working Paper No. 2465NBER (1987).

While interesting in their approach, the usefulness of the methodologiestaught by these studies in terms of valuing individual patent and otherintellectual property assets is limited. Such indirect market-basedvaluation approaches mostly attempt to establish only a generalizedcorrelation between stock prices of publicly traded companies and theaggregate number of intellectual property assets held or controlled bythose companies. Because individual stock prices are generallyreflective of the overall aggregated assets of a company and its futureearnings potential, such indirect market-valuation approaches do notlend themselves readily to valuing individual identified intellectualproperty assets. Moreover, intellectual property and other intangibleassets owned by publicly traded companies comprise only a fraction ofthe total population of potential intellectual property assets that maybe of interest.

A computer-automated variation of the traditional market approachspecifically adapted for rating patent portfolios is described in U.S.Pat. No. 5,999,907. In this case, a first database is providedcontaining information describing selected characteristics of aportfolio of patents to be acquired. A second database is providedcontaining empirical data describing selected characteristics ofrepresentative patent portfolios having known market values. Estimatedvaluations are obtained by comparing information in the first data baseto information in the second database to determine which known patentportfolio the portfolio to be acquired matches the closest. The value ofthe closest matching known portfolio is then used as a roughapproximation of the value of the portfolio to be acquired.

While such approach provides an innovative variation of the market-basedvaluation technique described above, it is again ultimately limited bythe need to acquire relevant market data of known patent portfolios. Asnoted above, such information is very difficult to obtain. Unless alarge amount of such data could be collected and analyzed, theeffectiveness and accuracy of such an approach would be very limited.Even if a large amount of such data could be collected and stored in asuitable computer-accessible database, the process of individuallyretrieving and comparing relevant characteristics of each representativeportfolio in the database would be undesirably time consuming, evenusing a high-speed computer. Moreover, the statistical accuracy of theresulting approximated valuations would be undetermined.

Income Approach

The third and perhaps most commonly used accounting-based approach forvaluing intellectual property and other intangible assets is theso-called income approach. This approach can provide accurate andcredible valuations of intellectual property assets in certainsituations where an isolated stream (or streams) of economic benefit canbe identified and attributed to an intellectual property asset inquestion. The income approach values an intellectual property asset bycapitalizing or discounting to present value all future projectedrevenue streams likely to be derived from its continued exploitation.For example, if a patent asset is licensed under an agreement thatprovides for a predictable income stream over a certain period of timeinto the future, then the intrinsic value of the patent may beaccurately calculated by taking the net discounted present value of theresidual income stream (less any scheduled maintenance costs).Similarly, if the patentee is directly exploiting the patent itself, theintrinsic value of the patent may be calculated by taking the netdiscounted value of the incremental profit stream (assuming it can beidentified) attributable to the patent over the remaining life of thepatent or the economic life of the patented technology.

In theory, the income valuation approach can produce very accurateestimates of intrinsic value for certain intellectual property and otherintangible assets. In practice, however, it is often difficult toidentify with certainty and precision an isolated income streamattributable to a particular intellectual property asset in question,let alone an income stream that is predictable over time. In addition,many intellectual property assets, particularly newly issued patents,are not licensed or exploited at all and, therefore, there are noidentifiable income streams upon which to base a valuation.

In such circumstances many asset valuation specialists attempt toproject possible or hypothetical future revenue streams or economicbenefits based on available data of other similar companies in theindustry and/or other license agreements for similar intellectualproperty assets in the same general technical field. Some patentvaluation experts have even established extensive data-bases of patentlicenses and have attempted to establish a schedule of “standard” orbaseline royalty rates or royalty ranges for patent licenses in variousindustries for purposes of forecasting possible future revenue streams.While such information can be very helpful, without an actualdemonstrated income stream or other proven economic benefit, theincome-based valuation approach loses credibility and can become morespeculation than valuation.

Each of the above valuation approaches has its characteristic strengthsand weaknesses. Of course, no single valuation method can provideabsolute certainty of the true intrinsic value of an asset. This isespecially true when valuing patents and other intangible intellectualproperty assets. Nevertheless, a need exists for a comparative valuationtechnique that overcomes the aforementioned problems and limitations andwhich does not require collecting comparative market data of existingpatent portfolios or calculating future hypothetical income streams orroyalty rates. There is a further need for an intellectual propertyvaluation method that produces statistically accurate valuations,ratings or rankings according to a determined statistical accuracy.

SUMMARY OF THE INVENTION

The present invention compliments and improves upon traditionalvaluation approaches by providing an objective, statistical-based ratingmethod and system for independently assessing the relative breadth(“B”), defensibility (“D”) and commercial relevance (“R”) of individualpatent assets and other intangible intellectual property assetsaccording to a determined statistical accuracy. Thus, the invention canbe used to provide new and valuable information that can be used bypatent valuation experts, investment advisors, economists and others tohelp guide future patent investment decisions, licensing programs,patent appraisals, tax valuations, transfer pricing, economicforecasting and planning, and even mediation and/or settlement of patentlitigation lawsuits.

In one embodiment the invention provides a statistically-based patentrating method and system whereby relative ratings or rankings aregenerated using a database of patent information by identifying andcomparing various characteristics of each individual patent to astatistically determined distribution of the same characteristics withina given patent population. For example, a first population of patentshaving a known relatively high intrinsic value or quality (e.g.successfully litigated patents) is compared to a second population ofpatents having a known relatively low intrinsic value or quality (e.g.unsuccessfully litigated patents). Based on a statistical comparison ofthe two populations, certain characteristics are identified as beingmore prevalent or more pronounced in one population group or the otherto a statistically significant degree. Multiple such statisticalcomparisons are used to construct and optimize a computer model orcomputer algorithm that can then be used to accurately predict and/orprovide statistically-accurate probabilities of a desired value orquality being present or a future event occurring, given the identifiedcharacteristics of an individual patent or group of patents.

The algorithm may comprise a simple scoring and weighting system whichassigns scores and relative weightings to individual identifiedcharacteristics of a patent or group of patents determined to havestatistical significance. For example, positive scores would generallybe applied to those patent characteristics having desirable influenceand negative scores would apply to those patent characteristics havingundesirable influence on the particular quality or event of interest. Ahigh-speed computer is then used to repeatedly test the algorithmagainst one or more known patent populations (e.g., patents declared tobe valid/invalid or infringed/non-infringed). During and/or followingeach such test the algorithm is refined by adjusting the scorings and/orweightings until the predictive accuracy of the algorithm is optimized.Once the algorithm is suitably optimized, selected metrics for anindividual identified patent or group of patents to be rated are inputinto the algorithm and the algorithm is operated to calculate anestimated rating or mathematical score for that patent or group ofpatents. Individual results could be reported as statisticalprobabilities of a desired quality being present, or a future eventoccurring (patent being litigated, abandoned, reissued, etc.) over aspecified period in the future. Results could also be provided asarbitrary raw scores representing the sum of an individual patent'sweighted scores, which raw scores can be further ranked and reported ona percentile basis within a given patent population and/or upon anyother comparative or non-comparative basis as desired.

The first and second patent populations selected for analysis arepreferably roughly the same size and may comprise essentially any twogroups of patents (or identifiable subsets of a single group of patents)having different actual or assumed intrinsic values or other qualitiesof interest. For example, the first population may consist of a randomsample of 500-1000 patents that have been successfully litigated (foundvalid and infringed) and the second population may consist of a randomsample of 500-1000 patents that have been unsuccessfully litigated(found either invalid or not infringed). Alternatively, the firstpopulation may consist of a random sample of patents that have beenlitigated and found valid regardless of whether infringement is alsofound, and the second population may consist of a random sample ofpatents that have been found invalid. Likewise, the first population mayconsist of a random sample of patents that have been litigated and foundinfringed regardless of the validity finding and the second populationmay consist of a random sample of patents that have been found notinfringed.

The selection of which study population(s) to use depends upon the focusof the statistical inquiry and the desired quality (e.g., claim scope,validity, enforceability, etc.) of the patent asset desired to beelicited. For example, if validity is the quality of interest, then thefirst and second patent populations may preferably be selected such thatone population is known or predicted to have a higher incidence ofinvalid patents than the other population. This information may bereadily gathered from published patent decisions of the Federal Circuitand/or the various federal district courts. Thus, the first populationmay consist of a random sample of patents declared invalid by a federalcourt and the second population may consist of a random sample ofpatents from the general patent population, which are presumed to bevalid. Alternatively, the second population may consist of a randomsample of patents declared “not invalid” by a federal court following avalidity challenge.

The approach is not limited, however, to analyzing litigated patents.For example, fruitful comparisons may also be made between litigatedpatents (presumably the most valuable patents) and non-litigatedpatents; or between high-royalty-bearing patents and low-royalty-bearingpatents; or between high-cost-basis patents and low-cost-basis patents;or between published patent applications and issued patents. The numberand variety of definable patent populations having different desiredqualities or characteristics capable of fruitful comparison inaccordance with the invention herein is virtually unlimited. While notspecifically discussed herein, those skilled in the art will alsorecognize that a similar approach may also be used for valuing and/orrating other intellectual property or intangible assets such astrademarks, copyrights, domain names, web sites, and the like.

In accordance with another embodiment the invention provides a methodfor rating or ranking patents. In accordance with the method, a firstpopulation of patents is selected having a first quality orcharacteristic and a second population of patents is selected having asecond quality or characteristic that is different from the firstquality or characteristic. Statistical analysis is performed todetermine or identify one or more patent metrics having either apositive or negative correlation with either said first or secondquality to a statistically significant degree. A regression model isconstructed using the identified patent metric(s). The regression modelis iteratively adjusted to be generally predictive of either the firstor second quality being present in a given patent. The regression modelis used to automatically rate or rank patents by positively weighting orscoring patents having the positively correlated patent metrics andnegatively weighting or scoring patents having the negatively correlatedpatent metrics (“positive” and “negative” being used here in therelative sense only). If desired, the method may be used to generate apatent rating report including basic information identifying aparticular reported patent or patents of interest and one or moreratings or rankings determined in accordance with the method describedabove.

In accordance with another embodiment the invention provides astatistical method for scoring or rating selected qualities ofindividual patents and for generating a rating report specific to eachindividual patent rated. The method begins by providing a first databaseof selected patent information identifying and/or quantifying certainselected characteristics of individual patents from a first populationof patents having a selected patent quality of interest. A seconddatabase (or identified subset of the first database) of selected patentinformation is also provided identifying and/or quantifying certainselected characteristics of individual patents from a second populationof patents generally lacking or having reduced incidence of the selectedpatent quality of interest. Statistical analysis is performed toidentify one or more characteristics that are statistically moreprevalent or more pronounced in either the first or second patentpopulation to a statistically significant degree. Based on thisinformation and the identified characteristics, individual patents maybe scored or rated by positively weighting those having the same orsimilar characteristics and negatively weighting those lacking the sameor similar characteristics. If desired, the method may be used togenerate a patent rating report including basic information identifyinga particular reported patent or patents of interest and one or moreratings or rankings determined in accordance with the method describedabove.

In accordance with another embodiment the invention provides a methodand automated system for rating or ranking patents or other intangibleassets. In accordance with the method a first population of patents isselected having a first quality or characteristic and a secondpopulation of patents is selected having a second quality orcharacteristic that is different from or believed to be different fromthe first quality or characteristic. A computer accessible database isprovided and is programmed to contain selected patent metricsrepresentative of or describing particular corresponding characteristicsobserved for each patent in the first and second patent populations. Acomputer regression model is constructed and adjusted based on theselected patent metrics. The regression model is operable to input theselected patent metrics for each patent in the first and second patentpopulations and to output a corresponding rating or ranking that isgenerally predictive of the first and/or second quality being present ineach patent in the first and second patent populations. The regressionmodel may then be used to rate or rank one or more patents in a thirdpatent population by inputting into the regression model selected patentmetrics representative of or describing corresponding characteristics ofone or more patents in the third population.

In accordance with another embodiment the invention provides ahigh-speed method for automatically scoring or rating a sequentialseries of newly issued patents as periodically published by the PTO andfor determining and storing certain rating or scoring informationspecific to each patent. According to the method, a substantial fulltext copy of each patent in the sequential series is obtained in acomputer text file format or similar computer-accessible format. Acomputer program is caused to automatically access and read eachcomputer text file and to extract therefrom certain selected patentmetrics representative of or describing particular observedcharacteristics or metrics of each patent in the sequential series. Theextracted patent metrics are input into a computer algorithm. Thealgorithm is selected and adjusted to produce a corresponding ratingoutput or mathematical score that is generally predictive of aparticular patent quality of interest and/or the probability of aparticular future event occurring. Preferably, for each patent in thesequential series the rating output or mathematical score is stored in acomputer accessible storage device in association with other selectedinformation identifying each rated patent such that the correspondingrating or score may be readily retrieved for each patent in thesequential series.

In accordance with another embodiment the invention provides a methodfor valuing individual selected patents. A patent value distributioncurve and/or data representative thereof is provided. The shape of thecurve generally represents an estimated distribution of patent valueaccording to percentile rankings within a predetermined patentpopulation. The area under the curve is generally proportional to thetotal approximated value of all patents in the predetermined patentpopulation. Individual selected patents from the population are rankedin accordance with selected patent metrics to determine an overallpatent quality rating and ranking for each individual selected patent.The patent value distribution curve is then used to determine acorresponding estimated value for an individual selected patent inaccordance with its overall patent quality ranking. If desired, themethod may be used to generate a patent valuation report including basicinformation identifying a particular reported patent or patents ofinterest and one or more valuations determined in accordance with themethod described above.

In accordance with another embodiment, the invention provides anautomated method for scoring or rating patents in accordance withuser-defined patent metrics and/or patent populations. The automatedmethod is initiated by a user selecting a patent, or group of patents,to be rated. A full-text computer accessible file of the patent to berated is retrieved from a central database, such as that currentlymaintained by the U.S. Patent & Trademark Office at www.uspto.gov. Acomputer algorithm evaluates the full-text file of the patent to berated and extracts certain selected patent metric(s), which may bepredefined, user-defined, or both. Based on the selected patentmetric(s), the algorithm computes a rating number or probability (e.g.,between 0 and 1) corresponding to the likely presence or absence of oneor more user-defined qualities of interest in the patent to be ratedand/or the probability of one or more possible future events occurringrelative to the patent. If desired, the rating number or probability canbe further ranked against other similar ratings for patents within aselected patent population, which may be predetermined, user-defined, orboth. Thus, the method in accordance with the preferred embodiment ofthe invention is capable of producing multiple independent ratingsand/or rankings for a desired patent to be rated, each tailored to adifferent user-defined inquiry, such as likelihood of the patent beinglitigated in the future, being held invalid, likelihood of successfulinfringement litigation, predicted life span of the patent, relativevalue of the patent, etc.

For purposes of summarizing the invention and the advantages achievedover the prior art, certain objects and advantages of the invention havebeen described herein above. Of course, it is to be understood that notnecessarily all such objects or advantages may be achieved in accordancewith any particular embodiment of the invention. Thus, for example,those skilled in the art will recognize that the invention may beembodied or carried out in a manner that achieves or optimizes oneadvantage or group of advantages as taught herein without necessarilyachieving other objects or advantages as may be taught or suggestedherein.

All of these embodiments and obvious variations thereof are intended tobe within the scope of the invention herein disclosed. These and otherembodiments of the present invention will become readily apparent tothose skilled in the art from the following detailed description of thepreferred embodiments having reference to the attached figures, theinvention not being limited to any particular preferred embodiment(s)disclosed.

BRIEF DESCRIPTION OF THE FIGURES

Having thus summarized the overall general nature of the invention andits features and advantages, certain preferred embodiments and exampleswill now be described in detail having reference to the figures thatfollow, of which:

FIG. 1 is a simplified schematic system block-diagram illustrating onepossible embodiment of a patent rating method and system having featuresand advantages in accordance with the present invention;

FIG. 2 is a simplified schematic flow chart of one possible multipleregression technique suitable for carrying out the rating method andsystem of FIG. 1;

FIG. 3 is a graph of percentages of litigated patents according to age,illustrating the declining incidence of patent litigation with patentage;

FIG. 4 is a graph of percentages of litigated patents found to beinfringed by a federal district court according to the average number ofwords per independent claim, illustrating the declining incidence ofpatent infringement with length of patent claim;

FIG. 5 is a graph of litigated patents according to technical field,illustrating the incidence of patent infringement holdings by field;

FIG. 6 is a graph of litigated patents according to technical field,illustrating the incidence of patent invalidity holdings by field;

FIG. 7 is a graph of percentages of litigated patents found to beinvalid by a federal district court according to the average age ofcited U.S. patent references, illustrating the declining incidence ofpatent invalidity with citation age;

FIG. 8 is a graph of overall patent maintenance rates for patents in thegeneral patent population, illustrating increasing rates of patentmortality with age;

FIG. 9 is a graph of patent mortality rates for patents having differentnumbers of claims, illustrating decreasing mortality rates withincreasing number of claims;

FIG. 10 is a graph of patent mortality rates for patents havingdifferent numbers of figures, illustrating decreasing mortality rateswith increasing number of figures;

FIG. 11 is one possible preferred embodiment of a patent rating reportgenerated in accordance with the method and system of FIG. 1 and havingfeatures and advantages of the present invention; and

FIG. 12 is one possible example of a patent value distribution curve foruse in accordance with one embodiment of a patent valuation method ofpresent invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The utility of the present invention begins with the fundamentalobservation that not all intellectual property assets are created equal.In the case of patent assets, for example, two patents even in the sameindustry and relating to the same subject matter can command drasticallydifferent royalty rates in a free market, depending upon a variety offactors. These factors may include, for example: (1) the premium orincremental cost consumers are willing to pay for products or servicesembodying the patented technology; (2) the economic life of the patentedtechnology and/or products; (3) the cost and availability of competingsubstitute technology and/or products; and (4) the quality of theunderlying patent asset.

The quality of a patent in terms of the breadth or scope of rightssecured, its defensibility against validity challenges and itscommercial relevance can have particularly dramatic impact on its value.Obviously, a patent that has a very narrow scope of protection or thatis indefensible against a validity challenge will have much less valuethan a patent that has a broad scope of protection and strongdefensibility. A skilled patent lawyer can examine the claims andspecification of a patent, its prosecution history and cited prior artand, based on a detailed legal analysis, render a subjective opinion asto the likely scope and defensibility of the patent. However, such legalwork is time-intensive and expensive. Thus, it may not be economicallyfeasible to consult with a patent lawyer in every situation where suchinformation may be desired.

The patent rating method and system of the present invention is notproposed to replace conventional legal analysis or traditional valuationmethods, but to complement and support the overall evaluative process.In one embodiment, the present invention provides an objective,statistical-based rating method and system for substantiallyindependently assessing the relative breadth (“B”), defensibility (“D”)and commercial relevance (“R”) of individual patent assets and otherintangible intellectual property assets. Thus, the invention can providenew and valuable information which can be used by patent valuationexperts, investment advisors, economists and others to help guide futurepatent investment decisions, licensing programs, patent appraisals, taxvaluations, transfer pricing, economic forecasting and planning, andeven mediation and/or settlement of patent litigation lawsuits. Suchinformation may include, for example and without limitation:statistically calculated probabilities of particular desired orundesired qualities being present; statistical probabilities of certainfuture events occurring relative to the asset in question; ratings orrankings of individual patents or patent portfolios; ratings or rankingsof patent portfolios held by public corporations; ratings or rankings ofpatent portfolios held by pre-IPO companies; ratings or rankings ofindividual named inventors; and ratings or rankings of professionalservice firms, law firms and the like who prepare, prosecute and enforcepatents or other intellectual property assets.

In its simplest form the present invention provides a statistical patentrating method and system for rating or ranking patents based on certainselected patent characteristics or “patent metrics.” Such patent metricsmay include any number of quantifiable parameters that directly orindirectly measure or report a quality or characteristic of a patent.Direct patent metrics measure or report those characteristics of apatent that are revealed by the patent itself, including its basicdisclosure, drawings and claims, as well as the PTO record or filehistory relating to the patent. Specific patent metrics may include, forexample and without limitation, the number of claims, number of wordsper claim, number of different words per claim, word density (e.g.,different-words/total-words), length of patent specification, number ofdrawings or figures, number of cited prior art references, age of citedprior art references, number of subsequent citations received, subjectmatter classification and sub-classification, origin of the patent(foreign vs. domestic), payment of maintenance fees, prosecutingattorney or firm, patent examiner, examination art group, length ofpendency in the PTO, claim type (i.e. method, apparatus, system), etc.

Indirect patent metrics measure or report a quality or characteristic ofa patent that, while perhaps not directly revealed by the patent itselfor the PTO records relating to the patent, can be determined or derivedfrom such information (and/or other information sources) using a varietyof algorithms or statistical methods including, but not limited to, themethods disclosed herein. Examples of indirect patent metrics includereported patent litigation results, published case opinions, patentlicenses, marking of patented products, and the like. Indirect patentmetrics may also include derived measures or measurement components suchas frequency or infrequency of certain word usage relative to thegeneral patent population or relative to a defined sub-population ofpatents in the same general field.

For example, each word and/or word phrase in a patent claim (and/orpatent specification) could be assigned a point value according to itsfrequency of use in a randomly selected population of similar patents inthe same general field. Statistically common words or word phrases suchas simple articles, pronouns and the like could receive relatively lowpoint values. Uncommon words or word phrases could receive relativelyhigh point values. The total point score for each claim could then betaken as an indication of its relative breadth or narrowness based onthe total number and statistical prevalence of each of the wordscontained in the claim. Optionally, different amounts of points can beaccorded to claim words or word phrases based on whether or not theyalso appear in the patent specification. Multiple claims and/or patentscould also be combined into a single analysis, if desired.

In accordance with one preferred embodiment of the invention relativeratings or rankings are generated using a database of selected patentinformation by identifying and comparing various relevantcharacteristics or metrics of individual patents contained in thedatabase. In one example, a first population of patents having a knownor assumed relatively high intrinsic value (e.g. successfully litigatedpatents) are compared to a second population of patents having a knownor assumed relatively low intrinsic value (e.g. unsuccessfully litigatedpatents). Based on the comparison, certain characteristics areidentified as statistically more prevalent or more pronounced in onepopulation group or the other to a significant degree.

These statistical comparisons are then used to construct and optimize acomputer model or computer algorithm comprising a series of operativerules and/or mathematical equations. The algorithm is used to predictand/or provide statistically determined probabilities of a desired valueor quality being present and/or of a future event occurring, given theidentified characteristics of an individual identified patent or groupof patents. The algorithm may comprise a simple scoring and weightingsystem which assigns scores and relative weightings to individualidentified characteristics of a patent or group of patents determined(or assumed) to have statistical significance. For example, positivescores could generally be applied to those patent characteristicsdetermined or believed to have desirable influence and negative scorescould be applied to those patent characteristics determined or assumedto have undesirable influence on the particular quality or event ofinterest.

Once the basic algorithm is constructed, a high-speed computer ispreferably used to repeatedly test the algorithm against one or moreknown patent populations (e.g. patents declared to be valid/invalid orinfringed/non-infringed). During and/or following each such test thealgorithm is refined (preferably automatically) by iteratively adjustingthe scorings and/or weightings assigned until the predictive accuracy ofthe algorithm is optimized. Adjustments can be made automatically in anorderly convergence progression, and/or they can by made randomly orsemi-randomly. The latter method is particularly preferred where thereare any non-linearities in the equations or rules governing thealgorithm. Algorithm results are preferably reported as statisticalprobabilities of a desired quality being present, or a future eventoccurring (e.g., patent being litigated, abandoned, reissued, etc.)during a specified period in the future. Algorithm results could also beprovided as arbitrary raw scores representing the sum of an individualpatent's weighted scores, which raw scores can be further ranked andreported on a percentile basis or other similar basis as desired.Preferably, the statistical accuracy of the algorithm is tracked andreported over time and periodic refinements are made as more and moredata is collected and analyzed.

System Architecture

FIG. 1 is a simplified block diagram of one possible embodiment of apatent rating method and automated system 100 having features andadvantages in accordance with the present invention. The system isinitiated at the START block 110. At block 120 certain characteristicsC_(a) of Patent Population “A” are inputted from a database 125 in theform:C_(a)={A₁,A₂ . . . A_(n})

where:

-   -   C_(a)=set of selected characteristics of Pat. Pop. “A”    -   A_(n)=an individual selected characteristic of Pat. Pop. “A”

At block 130 characteristics C_(b) of Patent Population “B” are inputtedfrom a database 135 in the form:C_(b)={B₁,B₂ . . . B_(n)}

where:

-   -   C_(b)=set of selected characteristics of Pat. Pop. “B”    -   B_(n)=an individual selected characteristic of Pat. Pop. “B”

Preferably, Patent Population “A” and Patent Population “B” are selectedto have different known or assumed intrinsic values and/or qualitiessuch that a fruitful comparison may be made. For example, Population “A”may comprise a random or semi-random (e.g., representative) sample ofsuccessfully litigated patents and/or individual patent claims.Population “B” may comprise a random or semi-random sample ofunsuccessfully litigated patents and/or individual patent claims. Inthat case, Population “A” patents/claims may be assumed to have higherintrinsic value than Population “B” patents/claims. Alternatively, andregardless of whatever assumed or intrinsic economic value the patentsmay have, Population “A” patents may be described as having the qualityof being successfully litigated (infringement or validity), whilstPopulation “B” patents may be described as having the quality of beingunsuccessfully litigated (infringement or validity). Thus, by examiningand comparing the characteristics of litigated patents/claims that fallinto either population “A” or “B”, one can make certain statisticalconclusions and predictions about other patents that may or may not havebeen litigated. Such probabilistic analysis can also be easily extendedto accurately calculate the odds, for example, of prevailing on aparticular patent infringement claim or defense in a particularlitigation proceeding (e.g., preliminary injunction motion, summaryjudgment motion, jury trial, bench trial, appeal, etc.). Suchinformation would be of tremendous value to patent litigants, forexample.

Of course, the study populations are not limited to litigatedpatents/claims. For example, one study population may comprise a randomor semi-random sample of patents selected from the general patentpopulation and having a representative “average” value or quality. Theother study population may comprise, for example and without limitation,a random or semi-random sample of patents selected from a sub-populationconsisting of all patents for which 1^(st), 2^(nd) or 3^(rd) maintenancefees have been paid; or all patents that have been licensed for morethan a predetermined royalty rate; or all patents that have beensuccessfully reissued/reexamined; or all patents that have relatedcounterpart foreign patents; or all patents that have been subsequentlycited by other patents at least X times; etc. The number and variety ofpossible ways to define study populations of interest in accordance withthe invention are virtually limitless.

Next, at block 140 a comparison is made between the selectedcharacteristics C_(a) of Patent Population “A” and the same selectedcharacteristics C_(b) of Patent Population “B”. Based on the comparison,certain characteristics are identified at block 144 as beingstatistically more prevalent or more pronounced in one population or theother to a significant degree. This comparison can be performed and thestatistical significance of observed differences determined by applyingknown statistical techniques. Thus, certain statistically relevantcharacteristics of each study population can be readily identified anddescribed mathematically and/or probabilistically.

At block 148 a multiple regression model is constructed using theidentified statistically relevant characteristics determined at block144. Multiple regression modeling is a well-known statistical techniquefor examining the relationship between two or more predictor variables(PVs) and a criterion variable (CV). In the case of the presentinvention the predictor variables (or independent variables) describe orquantify the selected relevant characteristics of a particular patentpopulation, e.g., class/sub-class, number of independent claims, numberof patent citations, length of specification, etc. Criterion variables(or dependent variables) measure a selected quality of a particularpatent population, such as likelihood of successful litigation (eithervalidity or infringement). Multiple regression modeling allows thecriterion variable to be studied as a function of the predictorvariables in order to determine a relationship between selectedvariables. This data, in turn, can be used to predict the presence orabsence of the selected quality in other patents. The regression modelhas the form:CV _(m) =ƒ{PV ₁ ,PV ₂ . . . PV _(n)}

where:

-   -   CV_(m)=criterion variable (e.g., quality desired to be        predicted)    -   PV_(n)=predictor variable (e.g., statistically relevant        characteristic)

Once the regression model is completed it can be applied at block 150 topredict the presence or absence of the selected quality in other patentsselected from Patent Population “C”, for example, which may be the sameas or different from Populations “A” or “B.” Characteristics C_(c) ofeach individual patent P_(n) to be analyzed are inputted at block 150from a database 155 in the form:C_(c)={C₁,C₂ . . . C_(n)}

where:

-   -   C_(c)=set of selected characteristics of a patent P_(n)    -   C_(n)=an individual selected characteristic of patent P_(n)

The relevant characteristics PV_(n) of patent P_(n) are identified andplugged into the regression model at block 160. The resulting predictedvalue or score CV_(m), representing the quality of interest for patentP_(n), is then outputted to a data output file 178, printer or otheroutput device, as desired. The system terminates at STOP block 180.

Statistical Methodology

Many different methods of statistical analysis may be suitably employedto practice the present invention. The preferred methodology is amultiple regression technique performed, for example, by a high-speedcomputer. As noted above, multiple regression modeling is a statisticaltechnique for examining the relationship between two or more predictorvariables (PVs) and a criterion variable (CV). In the case of thepresent invention the predictor variables (or independent variables)describe or quantify certain observable characteristics of a particularpatent population, e.g., number of independent claims, length ofspecification, etc. Criterion variables (or dependent variables) measurea selected quality of interest of a particular patent population, suchas likelihood of successful litigation, validity or infringement.Multiple regression modeling allows the criterion variable to be studiedas a function of the predictor variables in order to determine arelationship between selected variables. This data, in turn, can be usedto predict the presence or absence of the selected quality in otherpatents.

For example, if one were interested in examining the relationshipbetween the number of times the word “means” is used in a claim (the PV)and a finding of infringement in litigation (the CV), one could use thefollowing simple linear regression model:Y=a+bXi

Where:

-   -   Y=criterion variable (likelihood of patent infringement)    -   Xi=predictor variable (number of times “means” appears)    -   a=the Y-intercept (% found infringed where Xi=0)    -   b=the rate of change in Y given one unit change in Xi

The coefficients a, b can be determined by iteration or other means sothat the sum of squared errors is minimized in accordance with thewell-known ordinary least squares (OLS) technique. Given least squaresfit, the mean of the errors will be zero.

The above example is a single-variable, linear regression model. Incarrying out the present invention, those skilled in the art willreadily appreciate that it may be desirable to include a number ofdifferent predictor variables (PVs) in the regression model (expressedeither as linear or non-linear functions and/or rules) in order toextract useful information from available patent data. FIG. 2 is asimplified schematic flow chart 200 of one such suitable multipleregression technique that may be employed in carrying out the presentinvention.

The flow chart begins at the START block 202. At block 204 certainsystem variables are initialized. These include multi-regressioncoefficients a, b, c and d, incremental step changes Δa, Δb, Δc and Δdfor each coefficient a, b, c and d, respectively, and various countersCO (#correct predictions), IN (# incorrect predictions), n (# patent inpopulation) and m (loop repeat count). At step 206 the system inputsselected characteristics (C_(n)=X₁, X₂, X₃) of the next patent (n) inthe study population (e.g., litigated patents). Preferably, thecharacteristics X₁, X₂, X₃ have been previously selected and determinedto have a statistically significant impact on the selected patentquality desired to be measured. At step 208 the observed patent qualityY of patent n is inputted into the system. In this case, the patentquality of interest is the validity or invalidity of the patent asdetermined by a final judgment of a court. Alternatively, the measuredpatent quality could be any one or more of a number of other qualitiesof interest such as discussed above.

At step 210 the system calculates a predicted patent quality such as theprobability that the patent in question is valid P(valid). In this case,a simple linear multi-regression model is chosen having the form:P(valid)=a+bX ₁ +cX ₂ +dX ₃

where:

-   -   P(valid)=predicted probability of patent validity    -   X₁, X₂, X₃ are various predictor variables    -   a=Y-intercept (% found valid where X₁, X₂, X₃=0)    -   b, c, d=rate of change in P(valid) per unit change of X₁, X₂, X₃

Once the probability of validity is calculated, the system at step 212determines an expected quality Y′ based on the probability P(valid). Inparticular, if P(valid) is calculated to be greater than 0.5 (>50%) thenthe expected outcome Y′ is that the patent is “VALID” as indicated byblock 214. If P(valid) is calculated to be less than 0.5 (<50%) then theexpected outcome Y′ is that the patent is “INVALID” as indicated byblock 216.

The expected patent quality or outcome Y′ is then compared to the actualobserved patent quality Y at step 220 and a determination is madewhether Y=Y′ indicating a correct prediction (block 218) or whether Y< >Y′ indicating an incorrect prediction (block 222). In the case of acorrect prediction the counter CO is incremented. In the event of anincorrect prediction, the counter IN is incremented. If patent(n) is notthe last patent in the study population, then decision bock 226 directsthe system to loop back again repeating the above steps 206-226 for thenext patent n=n+1 in the population and incrementing the patent countern at block 224. If patent(n) is the last patent in the population(n=#pop) then decision block 226 directs the system to begin astatistical analysis of the regression model.

This analysis begins at block 228 wherein the statistical accuracy (SA)of the model (m) is calculated using the equation:SA(m)=CO/(CO+IN)

where:

-   -   SA(m)=statistical accuracy of regression model (m)    -   CO=number of correct predictions for model (m)    -   IN=number of incorrect predictions for model (m)

The statistical accuracy SA(m) is a simple and easily calculated measureof how much observed data was accurately accounted for (i.e. correctlypredicted) by the regression model (m). This is a very basic measure ofthe predictive accuracy of the regression model and is described hereinby way of example only. If desired, a more sophisticated approach, suchas variance analysis, could also be used to accurately measure thepredictive power of a given regression model (m).

Variance analysis measures the variance in the criterion variable (e.g.,Y′) as a function of each of the predictor variables (e.g., X₁, X₂, X₃).The measured variance in the criterion variable (Y′) can be broken intotwo parts: that predicted by one or more of the selected predictorvariables and that variance not predicted by the selected predictorvariables. The latter is often referred to as “error variance.” Thetotal predicted variance is the amount of variance accounted for by theregression model. For instance, if the predicted variance is 0.78—thismeans the regression model is accounting for 78% of the possiblevariance. Of course, it is important and desirable to account for asmuch variance as possible with a given regression model. The morevariance one can account for, the more confidence one has about thepredictions made by the regression model.

Predicted variance can also be increased by adding more predictorvariables to the regression model. But, as the number of predictorvariables in the regression model increases beyond a certain point thereis a risk that the predicted variance may become artificially inflated,indicating that the model is purporting to account for variance that isnot actually accounted for in the population. This problem may becontrolled by selecting an appropriate number of predictor variables ina given model in accordance with the number of samples in thepopulation. Preferably, the number of predictor variables is no morethan about 5-10% of the total number of samples in a given populationand is most preferably less than about 1-3% of the total population.Thus, for a patent population size of 1,000, preferably the number ofpredictor variables is no more than about 50-100 and most preferably nomore than about 10 to 30 total, or between about 15-25. Alternatively,where it is desirable to use more predictor variables in a givenregression model, an adjusted predicted variance may be calculated usingwell-known techniques which take into account both the number ofpredictor variables and the sample size.

Decision block 230 compares the calculated statistical accuracy SA(m) ofthe current regression model (m) to the statistical accuracy SA(m−1) ofthe previous regression model (m−1). If the statistical accuracy SA(m)indicates improvement, then decision block 230 directs the system tocoefficient adjustment block 227. This block increments or decrementsone or more of the coefficients (a, b, c and d) by a predeterminedamount (Δa, Δb, Δc and Δd). The adjustment amounts (+ or −) areperiodically determined by the system 200 to accurately converge theregression model toward maximum statistical accuracy SA. This may bedone in a variety of ways. One simple convergence technique is describedbelow.

If decision block 230 determines that SA(m)<SA(m−1), this indicates thatthe current regression model (m) is a worse predictor of the desiredpatent quality than the previous regression model (m−1). Therefore, adifferent adjustment is needed to be made to the coefficients a, b, c,and/or d in order to cause the system to reconverge toward the optimalsolution providing for maximum predictive accuracy. This is done bydirecting the system to blocks 232-268 to test the impact of variouschanges to each predictor variable (a, b, c, d) and to change one ormore of the coefficient adjustment amounts (Δa, Δb, Δc and Δd) asnecessary to reconverge on the optimal solution.

Preferably, course adjustments are made first and then finer and fineradjustments are continually made as the regression model converges on anoptimal solution having maximized statistical accuracy SA. Thus,decision blocks 232, 242, 252 and 262 first preferably determine whichof the adjustment amounts (Δa, Δb, Δc and Δd) is greatest in magnitude.For example, if it is determined that Δa is greater than each of theadjustment amounts Δb, Δc and Δd, then decision block 232 directs thesystem to block 234.

Block 234 tests a modified regression model (m−1) where a=a−Δa/2. If themodified regression model results in improved statistical accuracy suchthat:SA(TEST)>SA(m−1)then decision block 236 directs the system to block 238. Block 238inverts and reduces the adjustment amount Δa=−(Δa/2) and reinitializesthe counts CO and IN to zero. Block 240 reinitializes the patent countto n=1. The system then resumes normal operation starting at block 206.

If the modified regression model does not result in improved statisticalaccuracy, decision block 236 directs the system to the next decisionblock 242 to determine whether an adjustment to one of the othercoefficients might improve the accuracy of the regression model. Theprocess of adjusting the coefficients and testing the accuracy of a newadjusted regression model repeats until decision block 262 determinesthat the system has cycled through a predetermined number of models, inthis case m=1000. At this point the system stops at END block 270,whereby the data may be extracted and studied or used to provide qualityratings or rankings of patents outside (or inside) the study populationsas described above. If there are any non-linear relationships betweenthe criterion variable and any predictor variable(s), it is preferred torandomize the variable coefficients at least periodically and reconvergetoward an optimal solution in order to fully explore all possibleoptimal solutions.

Multiple regression modeling, as described above in connection with FIG.2, is particularly well suited to carrying out the rating methods of thepresent invention. The methodology allows one not only to determine astatistical relationship between a criterion variable (CV) of interestand a number of predictor variables (PVs), it also allows one todetermine the independent contributions of each predictor variable inthe model by allowing for partitioning of variance. In other words, onecan determine how much variance in the criterion variable is accountedfor by a specific predictor variable. This can be accomplished, forexample, by removing the PV in question from the model and thendetermining if the correlation predicted by the model significantlydeclines when the predictor variable is removed from the equation andthe other predictor variables remain.

Partitioning of variance is also useful in detecting possiblecollinearity or multi-collinearity between two of more predictorvariables. Collinearity occurs when all or most of the variance in onepredictor variable is accounted for by one other predictor variable.Multi-collinearity exists when several predictor variables combinedaccount for all or most of the variance of another predictor variable.While not directly detrimental to the utility of the invention,collinearity or multi-collinearity can create problems where it isdesired to accurately determine the slope or direction of an individualregression line for a particular predictor variable. Collinearity ormulti-collinearity can be reduced or eliminated by removing superfluouspredictor variables and/or by combining two or more predictor variablesinto a single normalized predictor variable.

EXAMPLE APPLICATIONS

Having thus described the preferred embodiments of the invention indetail those skilled in the art will recognize that the basic conceptsand principles disclosed herein may be applied and implemented in a widevariety of useful ways to achieve desired results. A few examples areprovided below by way of illustration in order to demonstrate thebroader utility of the invention and how it may be used commercially.

Example 1

One possible application of the present invention is to identify andstudy relevant characteristics from a sample of litigated patents todetermine and measure those patent metrics that are predictive of apossible future event, such as a patent being litigated. Patentlitigation is the ultimate attestation of patent value. A patentplaintiff is faced with enormous legal costs to bring and prosecute apatent infringement action. Thus, the decision to invest suchsubstantial sums to enforce a patent is potentially (although, notnecessarily) a strong indicator of the strength and value of theunderlying patent asset.

A study of statistical data representing about 1200 litigated patentsreveals several interesting patterns which can help predict whether aparticular patent will be litigated. One pattern that is immediatelyevident is that patents are typically litigated relatively early intheir lives. FIG. 3 is a graph of the average age of a selected sampleof litigated patents. This graph indicates that most patents (>50%) thatare litigated are litigated within five years from the date of issuance.The decrease in the incidence of patent litigation with age suggeststhat patents may have a diminishing value over time. This is generallyconsistent with what one might expect as newer technology replaces oldertechnology. Thus, using the graph of FIG. 3 and knowing the age of aparticular patent(s) of interest (all other things being assumed equal),one can estimate the probability of the patent(s) being litigated withinone year, two years, three years, etc., in the future.

Another interesting pattern is that foreign originating patents (i.e.,patents claiming priority to a foreign parent application) are much lesslikely to be litigated than domestic originating patents. For example, astudy of the relevant data reveals that 0.67% of all patents issued in1990 were litigated, compared to 0.16% of foreign originating patents.Moreover the incidence of patent litigation varies significantly withcountry of origin. Only 0.10% of all Japanese originating patents issuedin 1990 were litigated compared to 0.38% of U.K. originating patents andcompared to 0.15% of German originating patents. These differences mayreflect disparities in the relative costs of litigation for variousforeign patentees as well as language and cultural differences.

Each of the patent metrics identified above is anticipated to have astatistically significant impact on the probability of a patent beinglitigated in the future. By undertaking a statistical study of these andother patent metrics and by constructing a suitable regression model inaccordance with the invention disclosed herein, one can calculate anestimated statistical probability of a given patent being litigatedduring a predetermined period of time in the future based on theidentified patent characteristics. If desired, a numerical rating orranking may be assigned to each patent indicating the relativelikelihood of litigation.

Example 2

Another possible application of the present invention is to identify andstudy relevant characteristics from a sample of litigated patents todetermine and measure those patent metrics that are predictive of aparticular desired outcome in litigation (e.g., a finding ofinfringement and/or invalidity).

For example, it is a commonly-held notion among patent professionalsthat certain claim language or claim limitations can have narrowingeffects on the scope of patent claims. Claims that are very long andrecite many detailed limitations or that recite limitations in the formof “means plus function” language and the like can significantlyrestrict the scope of patent claims. Therefore, it is anticipated thatpatent metrics reflecting such qualities (e.g., large number of wordsper claim, or large number of different words per claim, use of “means”language and the like) will have a statistically significant negativecorrelation with favorable litigation results.

Table 1 below, summarizes the incidence of final judgments ofinfringement for 665 reported patent infringement cases brought in theU.S. federal district courts between 1987 and 1998. The results aredivided according to whether one or more of the asserted claim(s)contained a “means” limitation.

TABLE 1 Asserted Claim % Infringed “Means” 47.1 “Non-Means” 51.2

As indicated in Table 1, above, asserted patent claims that contained atleast one “means” limitation were found to be infringed about 8.7% (4.1%in absolute percentage terms) less often than asserted patent claimsthat did not contain a means limitation. This supports the notion that“means” limitations have a narrowing effect on claim breadth.

Similarly, FIG. 4 is a graph 320 of percentages of litigated patentsfound to be infringed by a federal district court between 1987 and 1998,illustrating a statistical relationship between the incidence ofinfringement and the average number of words or “word count” perindependent claim. The graph generally illustrates a declining incidenceof patent infringement with increasing word count. Again, this supportsthe generally-held notion that longer claims are narrower than shorterclaims. Of course, those skilled in the art will recognize that moresophisticated relationships could also be established and characterizedstatistically.

For example, a modified word count metric comprising only non-repeatedwords per claim could be used. Alternatively, each word and/or wordphrase in a patent claim could be assigned a point value according toits frequency of use in a randomly selected population of similarpatents in the same general field. Statistically common words or wordphrases such as simple articles, pronouns and the like would receiverelatively low point values. Uncommon words or word phrases wouldreceive relatively high point values. The total point score for eachclaim would then be an indication of its relative breadth or narrownessbased on the total number and statistical prevalence of each of thewords contained in the claim. Optionally, different amounts of pointscan be accorded to claim words or word phrases based on whether or notsuch words or word phrases also appear in the patent specification.Multiple claims and/or patents could also be combined into a single suchanalysis, if desired.

If multiple independent claims are being considered for each patent, itmay be helpful to develop a “relatedness index” metric whichcharacterizes the relatedness of each claim to one or more other claimsof the patent (and/or one or more other patents). All other things beingequal, it is expected that a patent having two or more claims that arehighly related to one another (e.g., having substantially overlappingclaim coverage) would be narrower in overall scope than a patent havingtwo or more claims that are substantially dissimilar from one another(and, therefore, likely cover different subject matter). One convenientway to formulate a relatedness index is to compare the number of wordsthat are common to each claim versus the number of words that are uniqueto each claim. For example, a first claim of interest (claim 1) maycontain 95% of the same words in common with a second claim of interest(claim 2). Therefore, the two claims could be described as having arelatedness index (R_(1,2)) of 95% or 0.95. However, a third claim ofinterest (claim 3) may contain only 45% of the same words in common withthe first claim (claim 1). Therefore, these two claims could bedescribed as having a relatedness index (R_(1,3)) of 45% or 0.45. Moresophisticated approaches could further weight or score each word inaccordance with frequency of use as described above, and/or couldprovide for matching of similar or synonymous words. A relatedness indexmetric could also be developed and used to compare the relatedness orapparent relatedness of one or more patent specifications. This could beuseful, for example, in identifying related or similar patents within aportfolio.

FIG. 5 is a graph 340 of litigated patents according to technical field,illustrating the incidence of patent infringement holdings by field.Similarly, FIG. 6 is a graph 360 of litigated patents according totechnical field, illustrating the incidence of patent invalidityholdings by field. In each case, the numbers above each bar indicate thesample size of each patent population reported. Each of these graphsillustrates a statistical relationship between the general technicalfield of an invention and the incidence of validity or infringementholdings in litigation.

FIG. 7 is a graph 380 of percentages of litigated patents found to beinvalid by a federal district court according to the average age of U.S.patent references cited therein. In particular, the graph 380illustrates a declining incidence of patent invalidity with citationage. Curve 390 is a representative trend line having the generalequation:Y=m×+B

where:

-   -   Y=Y-coordinate value (% infringement)    -   X=X-coordinate value (avg. age cited refs. in years)    -   m=slope of line (% infringement/#years)    -   b=Y-axis intercept

The slope (m) and Y-axis intercept (b) of curve 390 were determined bytrial and error to produce an ordinary least squares fit to the datareported by graph 380. Thus, the curve 390 (and the resulting formulathereof) is generally representative of the statistical relationshipbetween average citation age and incidence of patent validity inlitigation.

In each of the cases described above, the identified patent metrics areanticipated to have a statistically significant impact on theprobability of a patent being litigated successfully or unsuccessfully.By undertaking a statistical study of these and other identified patentmetrics and by constructing a suitable regression model in accordancewith the invention disclosed herein, one can accurately calculate anestimated statistical probability of a given patent being successfullylitigated (found valid and/or infringed), taking into consideration allof the identified patent characteristics and statistical relationshipssimultaneously. If desired, a numerical rating or ranking may beautomatically calculated and assigned to each patent indicating therelative likelihood of a particular event or quality. Such rating may beprovided for the patent as a whole or, alternatively (or in addition),individual ratings may be provided for one or more individual claims ofthe patent, as desired.

Example 3

In the United States and most foreign countries, patentees are requiredto pay periodic maintenance fees during the term of a patent to maintainthe patent in force. In most countries, these consist of fixed annualfees of $200-300 per year paid to the government patent office tomaintain a patent in force. In the United States, maintenance fees arepaid every four years and escalate progressively from $525/$1,050 tomaintain a patent in force beyond the fourth year, to $1,050/$2,100 tomaintain a patent in force beyond the eighth year, to $1,580/$3,160 tomaintain a patent in force beyond the twelfth year. Patentees thatqualify as a “small entity” pay the smaller amounts; all others pay thelarger amounts.

The relatively substantial and escalating nature of these periodicmaintenance fee payments has the effect of discouraging the maintenancefor the full-term of all but the most successful or valuable patents.Thus, such patent maintenance fee data provides a unique, introspectivelook at how patentees themselves value their own patents. A reasonableand economically motivated patentee would not pay to maintain his or herpatent if the cost of the maintenance fee exceeded the reasonableexpected future benefit likely to be gained by maintaining the patent inforce for an additional four year period. Thus, PTO records reflectingthe payment or non-payment of periodic maintenance fees by patenteesprovides a wealth of data from which a wide variety of usefulinformation may be derived. Such information is useful, for example, forpurposes of conducting patent valuations, patent rankings, patentratings, and/or for other purposes as generally taught herein.

Thus, another possible application of the present invention is toidentify and study relevant characteristics of a sample population of20,000-80,000 patents that have been maintained beyond the first, secondor third maintenance periods as against a sample population of20,000-80,000 patents that have not been maintained or are abandonedprior to the expiration of their statutory term. In this manner, one maydetermine and measure with a high-level of statistical accuracy (i.e.,greater than 95% confidence) those patent metrics that are predictive ofpatents being abandoned prior to expiration of their full term.Moreover, one may determine with a similar degree of statisticalaccuracy the particular relationship or contribution provided by one ormore individual patent metrics of interest. This may be accomplished,for example, using variance partitioning and/or other similarstatistical analysis techniques.

In this case, a study of the statistical data reveals severalinteresting patterns that may help predict whether a particular patentwill be abandoned or maintained beyond its first, second or thirdmaintenance period. FIG. 8 is a graph of patent maintenance rates for arandom sample population of patents issued in 1986. This graph 400indicates that approximately 83.5% of such patents were maintainedbeyond the fourth year, approximately 61.9% of the patents weremaintained beyond the eighth year and approximately 42.5% of the patentswere maintained beyond the twelfth year. In other words, all but about42.5% of the sample population were abandoned or allowed to expirebefore the full statutory patent term. This corresponds to an overallaverage patent mortality (abandonment) rate of approximately 58.5%. Fromthis and/or other similar data one can formulate certain generalexpectations or probabilities as to whether a patent will likely bemaintained or abandoned in the future.

More specific expectations and probabilities can be formulated byidentifying and/or measuring those specific patent metrics associatedwith patent populations having either high or low mortality rates. Forexample, the data reveals that Japanese originating patents generallyhave lower mortality rates than domestic originating patents (44.7% vs.58.5%). The data also reveals that patents classified by the PTO indifferent classes and/or subclasses can have significantly differentmortality rates. For example, Table 2 below illustrates various observedmortality rates for patents categorized in several selected PTO classes:

TABLE 2 CLASS DESCRIPTION MORTALITY 482 Exercise Equipment 79% 473 GolfClubs/Equipment 74% 434 Golf Training Devices 71% 446 Toys and AmusementDevices 70% 206/250 Packaging 57% 365/364 Computers 45% 935 GeneticEngineering 44%

As Table 2 illustrates, patent mortality rates can vary dramaticallydepending upon the general subject matter of the patented invention asdetermined by the PTO classification system. Thus, one can reasonablyconclude that, all other things being equal, certain classes ofinventions are probably more valuable (more likely to be maintained) orless valuable (less likely to be maintained) than certain other classesof inventions. From this and/or other similar data one can formulatespecific and/or more accurate expectations or probabilities as towhether a particular patent having certain identified characteristicswill likely be maintained or abandoned in the future.

FIG. 9 illustrates a similar observed correlation between the number ofclaims contained in a patent and the patent mortality rate. Inparticular, for patents having five or fewer claims the averagemortality rate is observed to be about 66.3%. However, for patentshaving greater than 25 claims the mortality rate is observed to drop to49.3%. Again, this indicates that, all other things being equal, patentshaving more claims are probably more valuable (more likely to bemaintained) than patents having less claims.

FIG. 10 illustrates another similar observed correlation between thenumber of figures or drawings contained in a patent and the patentmortality rate. In particular, for patents having five or fewer figuresthe average mortality rate is observed to be about 62.7%. However, forpatents having greater than 25 figures the mortality rate is observed todrop to 46.6%. Again, this indicates that, all other things being equal,patents having more figures (and presumably more disclosure) areprobably more valuable (more likely to be maintained) than patentshaving less figures.

At least one study has reported that the number of citationssubsequently received by a patent (“forward” citations) may also have apositive correlation with economic value. See, e.g., Harhoff et al.,“Citation Frequency and the Value of Patented Innovation, ZEW DiscussionPaper No. 97-27(1997). Assuming this is true, one would expect to see arelatively low mortality rate for patents that receive an above-averagenumber of forward citations and a relatively high mortality rate forpatents that receive a below-average number of forward citations. Thiscan be easily verified and statistically measured using the methodstaught herein.

Each of the patent metrics identified above is anticipated to have astatistically significant impact on the probability of a patent beingmaintained or abandoned, litigated successfully or unsuccessfully, etc.By undertaking a statistical study of these and other patent metrics andby constructing a suitable regression model or algorithm in accordancewith the invention disclosed herein, one can calculate with astatistically determined accuracy an estimated probability of aparticular patent quality or a particular event occurring affecting agiven patent. If desired, a numerical rating or ranking may be assignedto each patent indicating its relative value or score. Multiple ratingsor rankings may also be provided representing different qualities ofinterest or probabilities of particular future events occurring.

Patent Ratings, Valuations & Reports

Patent ratings or rankings as taught herein may be compiled and reportedin a variety of suitable formats, including numerical ratings/rankings,alphanumeric ratings/rankings, percentile rankings, relativeprobabilities, absolute probabilities, and the like. Multiple ratings orrankings may also be provided corresponding to different patentqualities of interest or specific patent claims. FIG. 11 illustrates onepossible form of a patent rating and valuation report 700 that may begenerated in accordance with a preferred embodiment of the invention.

As illustrated in FIG. 11, the report 700 contains some basic data 710identifying the patent being reported, including the patent number,title of the invention, inventor(s), filing date, issue date andassignee (if any). Several individual patent ratings 720 are alsoprovided, including overall patent breadth (“B”), defensibility (“D”),and commercial relevance (“R”). Breadth and Defensibility ratings arepreferably generated by a computer algorithm that is selected andadjusted to be predictive of known litigation outcomes (e.g.,infringement/non-infringement and validity/invalidity) of a selectedpopulation of litigated patents based on various comparative patentmetrics. Relevance ratings are preferably generated using a computeralgorithm selected and adjusted to be predictive of patent maintenancerates and/or mortality rates based on various comparative patent metricsincluding, preferably, at least one comparative metric based on anormalized forward patent citation rate (normalized according to patentage). If desired, each of the B/D/R ratings can be statisticallyadjusted relative to the remaining ratings using known statisticaltechniques so as to minimize any undesired collinearity or overlap inthe reported ratings.

In the particular example illustrated, ratings 720 are provided on ascale from 1 to 10. However, a variety of other suitable rating scalesmay also be used with efficacy, such as numerical rankings, percentilerankings, alphanumeric ratings, absolute or relative probabilities andthe like. If desired, individual ratings or rankings 720 may also becombined using a suitable weighting algorithm or the like to arrive atan overall score or rating 730 for a given patent, patent portfolio orother intellectual property asset. The particular weighting algorithmused would preferably be developed empirically or otherwise so as toprovide useful and accurate overall patent rating information for agiven application such as investment, licensing, litigation analysis,etc.

For investment purposes, for example, overall ratings may be provided inthe form of convenient bond-style ratings as summarized in Table 3below:

TABLE 3 Quality Rating Highest quality AAA High quality AA Medium-highquality A Upper medium quality BBB Medium quality BB Lower mediumquality B Medium-low quality CCC Low quality CC Lowest quality C

If desired, such overall ratings can be separately collected andtabulated for use as a handy reference source. For example, overallpatent ratings can be published and updated periodically for all patentscurrently in force and/or for all newly issued patents published by thePTO, providing simple and useful information to those who desire to useit. Such information could also advantageously be stored on a searchabledatabase accessible through an Internet-based web server or the like.

To accomplish this purpose, the invention may be modified and adapted toprovide high-speed, automated scoring or rating of a sequential seriesof newly issued patents periodically published by the PTO. According tothe preferred method, a substantial full-text copy of each patent in thesequential series is obtained in a computer text file format or similarcomputer-accessible format. A computer program is caused toautomatically access and read each computer text file and to extracttherefrom certain selected patent metrics representative of ordescribing particular observed characteristics or metrics of each patentin the sequential series. The extracted patent metrics are input into apreviously determined computer regression model or predictive algorithmthat is selected and adjusted to calculate a corresponding rating outputor mathematical score that is generally predictive of a particularpatent quality of interest and/or the probability of a particular futureevent occurring. Preferably, for each patent in the sequential series arating output or mathematical score is directly calculated from theextracted metrics using a series of predefined equations, formulasand/or rules comprising the algorithm. The results are then preferablystored in a computer accessible memory device in association with otherselected information identifying each rated patent such that thecorresponding rating may be readily referenced or retrieved for eachpatent in the sequential series.

Because the rating method in accordance with the modified embodiment ofthe invention described above directly calculates (for each patent orgroup of patents) the mathematical score or rating from the patentmetrics themselves, there is no need to access related stored data, suchas comparative representative patent data, from an associated database.Thus, the method can be carried out very rapidly for each patent in thesequential series. For example, using a high-speed computer executing apredetermined predictive algorithm the automated rating method describedabove can preferably be carried out in less than about 1-3 minutes perpatent, more preferably in less than about 30-45 seconds per patent, andmost preferably in less than about 5-10 seconds per patent. Moreover,because the predictive algorithm operates without requiring access toany comparative representative data, it may be easily stored,transferred, transported or otherwise communicated to others without theneed to also store, transfer, transport or communicate the underlyingcomparative data used to develop the algorithm.

While it is preferred to provide independent B/D/R ratings and/or anoverall score for each rated patent asset, those skilled in the art willrecognize that numerous other ranking or rating systems may be used withefficacy in accordance with the teachings herein. For example,individual patent/claim scores may be ranked relative to a givenpopulation such that ratings may be provided on a percentile basis.Alternatively, numerical and/or alphanumerical scores may be assigned ona scale from 1-5, 1-9, 1-10, or A-E, for example. Optionally, and asillustrated in FIG. 11, each claim of the reported patent may beanalyzed and rated separately if desired. In that case, each claim (1-9in the example illustrated in FIG. 11) is preferably indicated as beingeither independent (“I”) or dependent (“D”), as the case may be.Alternatively, only the independent claims of a reported patent may berated if desired.

Individual ratings 740, 750 and 755 in report 700 preferably providenumerical ratings (1-10) of the likely breadth (“B”), defensibility(“D”), and relevance (“R”) of each claim of the reported patent (and/orthe patent as a whole). Such “BDR” ratings may alternatively beexpressed in a variety of other suitable formats, such as letters,symbols, integer numerals, decimal numerals, percentage probabilities,percentile rankings, and the like. For example, a letter scoring system(e.g., A-E) could be assigned for each of the individual B/D/Rcomponents. In that case, a BDR rating of “B/A/A” would represent a “B”rating for breadth, and “A” ratings for both defensibility andrelevance. An overall rating could then be derived from the individualBDR component ratings using a suitable conversion index rating system asgenerally illustrated below in Table 4:

TABLE 4 BDR Rating Overall Rating A/A/A AAA A/A/x AA A/x/A AA x/A/A AAA/x/x A x/A/x A x/x/A A B/B/B BBB B/B/x BB B/x/B BB x/B/B BB B/x/x Bx/B/x B x/x/B B C/C/C CCC C/C/x CC C/x/C CC x/C/C CC C/x/x C x/C/x Cx/x/C C x/x/x D

In the above Table 4, “x” represents an individual component rating(either B, D or R) that is lower than the highest of the remainingrating component(s) such that only the highest component rating(s) arereflected in the overall rating. Thus, a BDR rating of A/A/B or A/B/Awould each produce an overall rating of “AA.” Likewise, a BDR rating ofC/B/C or B/D/E would each produce an overall rating of “B.” Optionally,various additional rules and/or weighting formulas may be used to adjustthe overall rating assigned in accordance with this system. For example,if one or more of the low component ratings “x” is two or more ratinglevels below the highest component rating(s) then the overall rating canbe decreased by one increment. Thus, a BDR rating of C/B/C would producean overall rating of “B” whilst a BDR rating of B/D/E would produce anoverall rating of “CCC” or “CC”. Preferably, if no individual componentrating is at least a “C” (or other predetermined rating level) orhigher, then the overall rating is assigned some arbitrary baselinerating, such as “D” or “C” or “S” and/or the like.

Preferably, estimated maintenance rates 760 are also provided and areindicated as percentage probabilities for each maintenance period.Alternatively, maintenance data may be provided in a number of othersuitable formats, as desired, such as percentile rakings, absolute orrelative probabilities and the like. Also, various confidence levels maybe calculated and displayed for each of the reported probabilities 760,if desired.

Optionally, the report 700 may further include an estimated valuationrange 770 or expected value of the reported patent. Such patentvaluation 770 may be based on a variety of suitable techniques thatpreferably take into account the rating information provided herein. Forexample, a modified cost-basis approach could be used whereby thecost-basis is multiplied by a suitable discount or enhancement factorcorresponding to the rating(s) that the patent receives in accordancewith the methods disclosed herein. In this manner, patents that receivehigher-than-average ratings would be valued at more than their costbasis. Conversely, patents that receive lower-than-average ratings wouldbe valued at less than their cost basis.

Similarly, a modified income valuation approach could be used whereby ahypothetical future projected income stream or average industry royaltyrate is multiplied by a suitable discount or enhancement factorcorresponding to the rating that the patent receives in accordance withthe methods disclosed herein. In this manner, patents that receivehigher ratings would be valued at higher than industry averages.Conversely, patents that receive lower ratings would be valued at lowerthan industry averages.

Another preferred approach would be to allocate patent value based on apercentile ranking of patents as determined herein. For this approach anapproximated distribution of relative patent values is determined fromexisting patent renewal data, patent litigation data and/or the like.From this data, a value distribution curve can be constructed such asillustrated in FIG. 12. The shape of the curve generally represents anestimated distribution (e.g., on a percentile basis) of approximatedpatent values spread over a range from the very highest-value patents tothe very lowest-value patents. See also, Hall, “Innovation and MarketValue,” Working Paper 6984 NBER (1999) (suggesting an extremely skewedvalue distribution whereby a few patents are extremely valuable, whilemany others are worth little or almost nothing). The area under thecurve 800 preferably corresponds to the total estimated value of allpatents in a given patent population (e.g., all U.S. patents currentlyin force). This can be readily estimated or approximated by applyingsuitable macro-economic analysis. For example, it may be approximated asa percentage of total GNP, or as a percentage of total marketcapitalization of publicly traded companies, or as a multiple of annualbudgeted PTO fees and costs, and/or the like.

Patents having the highest percentile rankings in accordance with therating methods disclosed herein would then be correlated to the high endof the value distribution curve 800. Conversely, patents having thelowest percentile rankings in accordance with the rating methodsdisclosed herein would then be correlated to the low end of the valuedistribution curve 800. Advantageously, such allocative valuationapproach brings an added level of discipline to the overall valuationprocess in that the sum of individual patent valuations for a givenpatent population cannot exceed the total aggregate estimated value ofall such patents. In this manner, fair and informative valuations can beprovided based on the relative quality of the patent asset in questionwithout need for comparative market data of other patents or patentportfolios, and without need for a demonstrated (or hypothetical) incomestreams for the patent in question. Estimated valuations are basedsimply on the allocation of a corresponding portion of the overallpatent value “pie” as represented by each patents' relative ranking orposition along value distribution curve 800.

Alternatively, any one or more of the above valuation techniques (orother techniques) can be combined or averaged to produce appropriatevaluation ranges and/or various blended valuation estimates, as desired.Various confidence levels may also be calculated and reported for eachof the reported value ranges 770. Alternatively, several different valueranges can be calculated according to different desired confidencelevels.

Internet Applications

The present invention is ideally suited for Internet-based applications.In one preferred embodiment, the invention would be made available toInternet users on the World Wide Web (“the web”), or a similar publicnetwork, and would be accessible through a web page. Various services,embodying different aspects of the present invention, could be madeavailable to users on a subscription or a pay-per-use basis.

In an Internet-based application, users would preferably have access toautomated patent ratings, consolidated patent ratings (i.e. grouped bytechnology, business sector, industry, etc.), and a host of ancillaryinformation regarding particular patents or groups of patents. Ancillaryinformation may include, for example, full-text searchable patent files,patent images, bibliographic data, ownership records, maintenancerecords, and the like. A user would preferably be able to enter or“click” on the number of a patent he or she was interested in andobtain, in very short order (e.g., in less than about 1-5 minutes), acomprehensive rating report as described above. Preferably, the userwould be able to control most, if not all, of the variables in therating calculation. Thus, for instance, he or she could request that thepatent be rated only against other patents in the same art group, or ina specific industry, or in a particular field of use. He or she couldrequest a report on how the patent compares to all patents that havebeen litigated in the past 5 years, or that have been held invalid byU.S. courts. In this manner, reports could be narrowly tailored to thespecific interests and concerns of the user. This is beneficial—thoughnot critical—because different types of users, e.g., lawyers,businessmen, manufacturers, investors, etc., will have slightlydifferent appraisal needs.

In another preferred embodiment, it is not necessary that a useractually know the patent number or title of the patent he or she wishesto have rated. Instead, this preferred embodiment would include a seriesof correlation tables which allow the user to retrieve patent numbersbased on ownership, field of use, or even specific commercial products.Thus, it would be possible for a user to request reports on all patentsthat have been issued or assigned to a particular company in the past 5years.

Ideally, it would also be possible for a user to request reports on allpatents associated with a specific commercial product. Such productpatent information could advantageously be collected and stored on acentralized, searchable computer network database or the like in orderto allow users to search and obtain patent information on particularcommercial products. Relevant patent marking data could be gatheredeither through private voluntary reporting by manufacturers of suchproducts and/or it may be gathered through other available means, suchas automated web crawlers, third-party reporting or inputting and thelike. Patent marking data (e.g., the presence or absence of a patentnotice on a corresponding commercial product) and/or other relevant data(e.g., sales volume, sales growth, profits, etc.) could provideadditional objective metric(s) by which to rate relevant patents inaccordance with the invention. Presumably, patents that are beingactively commercialized are more valuable than “paper patents” for whichthere is no corresponding commercial product. Optionally, the patentmarking database can also include the necessary URL address informationand/or the like which will allow users to hot-link directly to athird-party web page for each corresponding product and/or associatedproduct manufacturer.

In another embodiment of the invention, users would be allowed torequest automatic updates and patent ratings according to certainuser-defined parameters. Thus, a user who is particularly interested inthe XYZ company could request an automatic updated report—sent to himsubstantially contemporaneously (preferably within a few days, morepreferably within about 2-3 hours, and most preferably within less thanabout 5-10 minutes) via e-mail and/or facsimile—whenever the XYZ companyobtains a newly issued patent. A similar updated report could begenerated and sent any time a new patent issued or a new application ispublished in a particular technology field or class of interest. Theupdates would preferably contain a synopsis of each new patent orpublished application, as well as a patent rating performed according tothat user's preferred criteria. Updated reports for each rated patentcould also be generated periodically whenever one or more identifiedpatent metrics changed (e.g., forward citation rate, change ofownership, litigation, etc.). Such automated updating of ratinginformation would be particularly important to investment and financialanalysts, who depend on rapid and reliable information to makeminute-by-minute decisions. Updated report(s) could also be generatedand published each week for all newly issued patents granted by the PTOfor that current week. Thus, in accordance with one preferred embodimentof the invention, informative patent rating and/or ranking informationmay be provided within days or hours of a new patent being issued andpublished by the PTO.

Another service that may be provided in a preferred Internet-basedapplication of this invention is a user-updated information database.According to this embodiment, certain users and/or all users would beallowed to post information they believe is pertinent to a particularpatent or group of patents. Such information might include prior artthat was not cited in the patent, possible license terms, potentialproblems with the written description or claims of the patent,information about the inventors, information relating to sales ofpatented products prior to the filing date, legal opinions, relatedlitigation, and any other information that might be relevant to thepatent. The information would preferably be stored and displayed inassociation with each particular patent to which it is relevant. Thus,from the user's perspective each patent would, in effect, have its ownbulletin board or note pad associated with it, upon which users may postrelevant information. Other information could also be displayed, such aslicense terms available, commercial product information, other patentsof interest, electronic file wrappers, hot-links to other sites, and thelike.

Optionally, submitters could also provide their own rating or ranking ofthe patent in question, such that patents could be essentiallyself-rated by users. In the preferred embodiment, only qualified users(or selected patent analysts) would be allowed to post such ratings. Thequalification process could be as simple as filling out a questionnaireor as thorough as an independent verification of credentials. It is alsopossible to employ the methodology currently used by such web sites as“epinions.com” to track the popularity and veracity of individualuser-submitted information and determine which users are most trusted.Those users that are most trusted would be brought to the top of thepatent information database and their authors compensated according tothe number of times users accessed the information, while less-popularsubmitters' information would sink in rank. Users and/or analysts couldalso be compensated financially (or otherwise) based on the accuracy oftheir ratings relative to the collective rating prediction and/orrelative to the occurrence of a predicted future event. This wouldmotivate more careful analysis and more accurate ratings. See, U.S. Pat.No. 5,608,620, incorporated herein by reference, for a description of acollective prediction and forecasting method using multiple individualforecasters, which may be readily adapted and applied to the presentinvention as disclosed herein.

The present invention is also well suited for incorporation into anewsletter service, such as the numerous financial newsletters currentlyavailable to Wall Street investors. In this embodiment of the invention,the rating system described herein would preferably be applied to apre-defined subset of issued patents—for instance, all patents newlyissued to “Fortune 500” companies or designated “Pre-IPO” companies.Overall patent ratings would be denoted with a standardized system, suchas a 1-10 scale, four stars, bond-style ratings, “BDR” ratings and/orthe like. Preferably, requested reports would be automatically generatedand e-mailed to each subscriber on a periodic basis and/or on anevent-triggered basis, as desired. In this way, subscribers would beprovided with a standardized method of comparing patent portfolios ofvarious companies from week to week.

While the statistical rating method and system of the present inventionis disclosed and discussed specifically in the context of rating utilitypatents, those skilled in the art will readily appreciate that thetechniques and concepts disclosed herein may have equal applicability torating other types of intellectual property assets, such as trademarks,copyrights, trade secrets, domain names, web sites and the like.Moreover, although this invention has been disclosed in the context ofcertain preferred embodiments and examples, it will be under-stood bythose skilled in the art that the present invention extends beyond thespecifically disclosed embodiments to other alternative embodimentsand/or uses of the invention and obvious modifications and equivalentsthereof. Thus, it is intended that the scope of the present inventionherein disclosed should not be limited by the particular disclosedembodiments described above, but should be determined only by a fairreading of the claims that follow.

1. A computer-implemented automated method for enabling a user to accessand operate a predetermined predictive computer model to score or rate aselected patent or group of patents in accordance with user-definedpatent metrics, comprising: selecting a patent or group of patents to berated; obtaining by a computer system from a database for each selectedpatent or group of patents a substantial full-text computer accessiblefile of the written description and/or claims thereof; extracting fromeach said file certain patent metrics, either predetermined or asselected or directed by said user; inputting said patent metrics intosaid predictive computer model, said predictive computer model beingoperable to input said patent metrics and based thereon to calculate acorresponding output rating or probability that is generally predictiveof a particular quality being present and/or an event occurring relativeto the selected patent or group of patents, wherein said predictivecomputer model comprises a multiple regression model that correlatesmultiple individual predictor variables comprising said extracted patentmetrics to one or more desired criterion variables comprising thecorresponding output rating or probability; and generating by thecomputer system a patent rating report for said selected patent or groupof patents, said patent rating report including basic informationidentifying said selected patent or group of patents and saidcorresponding output rating or probability determined therefore; whereinsaid patent rating report contains at least one reported rating orranking that is generally representative of the breadth (“B”) or likelyinfringement of the selected patent or group of patents, at least onereported rating or ranking that is generally representative of thedefensibility (“D”) or likely validity of the selected patent or groupof patents, and at least one reported rating or ranking that isgenerally representative of the commercial relevance (“R”) or technicalmerit of the selected patent or group of patents.
 2. Thecomputer-implemented method of claim 1 comprising the further step ofranking said output rating or probability against other output ratingsor probabilities calculated for other patents within a predetermined oruser-defined patent population.
 3. The computer-implemented method ofclaim 2 comprising the further step of storing relevant user-providedinformation pertaining to said selected patent or group of patents in acomputer-accessible database in association with other informationidentifying said selected patent or group of patents.
 4. Thecomputer-implemented method of claim 3 wherein said user-providedinformation comprises patent marking information and/or informationpertaining to patented products.
 5. The computer-implemented method ofclaim 3 wherein said user-provided information comprises one or more ofthe following: relevant prior art that was not cited in the selectedpatent or group of patents, license terms, information about namedinventors, information relating to sales of patented products, legalopinions, related litigation, other related or unrelated patents ofinterest, electronic file wrappers, hot-links to web sites.
 6. Thecomputer-implemented method of claim 3 wherein said user-providedinformation comprises user-provided ratings or rankings of said selectedpatent or group of patents.
 7. The computer-implemented method of claim3 comprising the further step of displaying said stored user-providedinformation pertaining to said selected patent or group of patents inassociation with said selected patent or group of patents such that eachselected patent comprises its own bulletin board or note pad associatedwith it, upon which users may post and/or obtain relevant information.8. The computer-implemented method of claim 3 wherein said extractedpatent metrics comprise one or more characteristics of said selectedpatent or group of patents that are determined or assumed to have eithera positive or negative correlation with the presence or absence of saidparticular quality and/or said event occurring relative to the selectedpatent or group of patents.
 9. The computer-implemented method of claim1 wherein said extracted patent metrics include one or more of thefollowing: number of claims per patent, number of words per claim,different words per claim, length of patent specification, number ofdrawing pages or figures, number of cited prior art references, age ofcited references, number of subsequent citations received, subjectmatter classification and sub-classification, origin of the patent,payment of maintenance fees, name of prosecuting attorney or law firm,examination art group, or length of pendency in a patent office.
 10. Thecomputer-implemented method of claim 1 wherein said extracted patentmetrics include one or more of the following: patent marking data, claimrelatedness, patent relatedness, or claim type.
 11. Thecomputer-implemented method of claim 1 wherein at least one of saidextracted patent metrics comprises a modified claim word-count metricwhereby each word and/or words, phrase in a patent claim of interest isassigned a certain point value generally proportional to its determinedfrequency of use in a relevant patent population and wherein theword-count metric is set equal to the sum of each of the individual wordpoint values for essentially all of the words or word phrases containedwithin said claim.
 12. The computer-implemented method of claim 1wherein at least one of said extracted patent metrics comprises arelatedness metric generally indicative of the commonality of word orword phrase usage between one or more patent claims and/or patentwritten description.
 13. The computer-implemented method of claim 1wherein said multiple regression model has the form:CV_(m)=ƒ{PV₁,PV₂ . . . PV_(n)} where: CV_(m)=criterion variable orquality/event desired to be predicted PV_(n)=predictor variables orselected patent metrics.
 14. The computer-implemented method of claim 1wherein said regression model includes between about 15 and 25 predictorvariables.
 15. The computer-implemented method of claim 1 comprising thefurther step of determining the statistical accuracy of the regressionmodel in accordance with the general formula:SA(m)=CO/(CO+IN) where: SA(m)=statistical accuracy of regression model(m) CO=number of correct predictions for model (m) IN=number ofincorrect predictions for model (m).
 16. The method of claim 1 whereinsaid patent rating report is generated in response to an electronicrequest transmitted over a computer network and wherein said report isgenerated and displayed automatically without further humanintervention.
 17. The method of claim 1 comprising the further step of,after generating said report, automatically without further humanintervention transmitting said report electronically over a computernetwork to one or more intended recipients.
 18. A computer-implementedmethod for rating a patent or group of patents, said method comprising:gathering by a computer system data comprising one or more selectedmetrics generally identifying and/or quantifying certain selectedcharacteristics of said patent or group of patents to be rated;providing by the computer system said data as input to a mathematicalrating model; using by the computer system the mathematical rating modelto calculate a numerical score or rating based on the data input, saidmathematical rating model being statistically determined such that saidnumerical score or rating is generally predictive of the probability ofsaid patent or group of patents being maintained or abandoned in thefuture, wherein said mathematical rating model comprises a multipleregression model that correlates multiple individual predictor variablescomprising said selected metrics to one or more desired criterionvariables comprising the corresponding output rating and/or probabilityof the patents being maintained or abandoned; generating by the computersystem a patent rating report for said selected patent or group ofpatents, said report including basic information identifying saidselected patent or group of patents and said corresponding output ratingor probability determined therefore; wherein said patent rating reportis generated in response to an electronic request transmitted over acomputer network and wherein said report is generated and displayedautomatically without further human intervention; and wherein saidpatent rating report contains at least one reported rating or rankingthat is generally representative of the breadth (“B”) or likelyinfringement of the selected patent or group of patents, at least onereported rating or ranking that is generally representative of thedefensibility (“D”) or likely validity of the selected patent or groupof patents, or at least one reported rating or ranking that is generallyrepresentative of the commercial relevance (“R”) or technical merit ofthe selected patent or group of patents.
 19. The computer-implementedmethod of claim 18 wherein said mathematical rating model is derived bystoring a first group of data comprising selected metrics identifyingand/or quantifying said selected characteristics of a first populationof patents for which maintenance fees have been paid, storing a secondgroup of data comprising selected metrics identifying and/or quantifyingsaid selected characteristics of a second population of patents forwhich maintenance fees have not been paid or for which it isundetermined whether maintenance fees have been paid, and using astatistical regression to create a predictive algorithm based on saidstored first and second groups of data.
 20. The computer-implementedmethod of claim 18 wherein said selected metrics include one or more ofthe following: number of claims per patent, number of words per claim,different words per claim, length of patent written description, numberof drawing pages or figures, number of cited prior art references, ageof cited references, number of subsequent citations received, subjectmatter classification and sub-classification, origin of the patent,payment of maintenance fees, name of prosecuting attorney or law firm,examination art group, or length of pendency in a patent office.
 21. Thecomputer-implemented method of claim 18 wherein said multiple regressionmodel has the form:CV_(m)=ƒ{PV₁,PV₂ . . . PV_(n)} where: CV_(m)=criterion variable orquality/event desired to be predicted PV_(n)=predictor variables orselected patent metrics.
 22. The computer-implemented method of claim 18wherein said regression model includes between about 15 and 25 predictorvariables.
 23. The computer-implemented method of claim 18 comprisingthe further step of determining the statistical accuracy of theregression model in accordance with the general formula:SA(m)=CO/(CO+IN) where: SA(m)=statistical accuracy of regression model(m) CO=number of correct predictions for model (m) IN=number ofincorrect predictions for model (m).
 24. A computer-implemented methodfor scoring or rating a selected patent or group of patents comprising:accessing computer-executable instructions from at least onecomputer-readable storage medium; and executing the computer-executableinstructions, thereby causing computer hardware comprising at least onecomputer processor to perform operations comprising: selecting a patentor group of patents to be rated; obtaining for each selected patent orgroup of patents a substantial full-text computer accessible file of thewritten description and/or claims thereof; extracting from each saidfile certain patent metrics, either predetermined or as selected ordirected by said user; inputting said patent metrics into saidpredictive computer model, said predictive computer model being operableto input said patent metrics and based thereon to calculate acorresponding output rating or probability that is generally predictiveof a particular quality being present and/or an event occurring relativeto the selected patent or group of patents, wherein said predictivecomputer model comprises a multiple regression model that correlatesmultiple individual predictor variables comprising said extracted patentmetrics to one or more desired criterion variables comprising thecorresponding output rating or probability; and generating a patentrating report for said selected patent or group of patents, said patentrating report including basic information identifying said selectedpatent or group of patents and said corresponding output rating orprobability determined therefore; wherein said patent rating reportcontains at least one reported rating or ranking that is generallyrepresentative of the breadth (“B”) or likely infringement of theselected patent or group of patents, at least one reported rating orranking that is generally representative of the defensibility (“D”) orlikely validity of the selected patent or group of patents, or at leastone reported rating or ranking that is generally representative of thecommercial relevance (“R”) or technical merit of the selected patent orgroup of patents.
 25. The computer-implemented method of claim 24,wherein the operations further comprise ranking said output rating orprobability against other output ratings or probabilities calculated forother patents within a predetermined or user-defined patent population.26. The computer-implemented method of claim 24, wherein the operationsfurther comprise storing relevant user-provided information pertainingto said selected patent or group of patents in a computer-accessibledatabase in association with other information identifying said selectedpatent or group of patents.
 27. The computer-implemented method of claim26 wherein said user-provided information comprises patent markinginformation and/or information pertaining to patented products.
 28. Thecomputer-implemented method of claim 26 wherein said user-providedinformation comprises one or more of the following: relevant prior artthat was not cited in the selected patent or group of patents, licenseterms, information about named inventors, information relating to salesof patented products, legal opinions, related litigation, other relatedor unrelated patents of interest, electronic file wrappers, hot-links toweb sites.
 29. The computer-implemented method of claim 26 wherein saiduser-provided information comprises user-provided ratings or rankings ofsaid selected patent or group of patents.
 30. The computer-implementedmethod of claim 26, wherein the operations further comprise displayingsaid stored user-provided information pertaining to said selected patentor group of patents in association with said selected patent or group ofpatents such that each selected patent comprises its own bulletin boardor note pad associated with it, upon which users may post and/or obtainrelevant information.
 31. The computer-implemented method of claim 26wherein said extracted patent metrics comprise one or morecharacteristics of said selected patent or group of patents that aredetermined or assumed to have either a positive or negative correlationwith the presence or absence of said particular quality and/or saidevent occurring relative to the selected patent or group of patents. 32.The computer-implemented method of claim 24 wherein said extractedpatent metrics include one or more of the following: number of claimsper patent, number of words per claim, different words per claim, lengthof patent specification, number of drawing pages or figures, number ofcited prior art references, age of cited references, number ofsubsequent citations received, subject matter classification andsub-classification, origin of the patent, payment of maintenance fees,name of prosecuting attorney or law firm, examination art group, orlength of pendency in a patent office.
 33. The computer-implementedmethod of claim 24 wherein said extracted patent metrics include one ormore of the following: patent marking data, claim relatedness, patentrelatedness, or claim type.
 34. The computer-implemented method of claim24 wherein at least one of said extracted patent metrics comprises amodified claim word-count metric whereby each word and/or words, phrasein a patent claim of interest is assigned a certain point valuegenerally proportional to its determined frequency of use in a relevantpatent population and wherein the word-count metric is set equal to thesum of each of the individual word point values for essentially all ofthe words or word phrases contained within said claim.
 35. Thecomputer-implemented method of claim 24 wherein at least one of saidextracted patent metrics comprises a relatedness metric generallyindicative of the commonality of word or word phrase usage between oneor more patent claims and/or patent written description.
 36. Thecomputer-implemented method of claim 24 wherein said multiple regressionmodel has the form:CV_(m)=ƒ{PV₁,PV₂ . . . PV_(n)} where: CV_(m)=criterion variable orquality/event desired to be predicted PV_(n)=predictor variables orselected patent metrics.
 37. The computer-implemented method of claim 24wherein said regression model includes between about 15 and 25 predictorvariables.
 38. The computer-implemented method of claim 24, wherein theoperations further comprise determining the statistical accuracy of theregression model in accordance with the general formula:SA(m)=CO/(CO+IN) where: SA(m)=statistical accuracy of regression model(m) CO=number of correct predictions for model (m) IN=number ofincorrect predictions for model (m).